Master Machine Learning Algorithms

Discover How They Work and Implement Them From Scratch

A gentle introduction to the procedures to learn models from data for 10 popular and useful supervised machine learning algorithms used for predictive modeling. Each algorithm includes one or more step-by-step tutorials explaining exactly how to plug in numbers into each equation and what numbers to expect as output. Each tutorial is designed to be completed in a spreadsheet.

Intermediate Ebooks

Machine Learning Mastery With Weka

Analyze Data, Develop Models and Work Through Projects

Discover how to load data, transform data, evaluate machine learning algorithms and work through machine learning projects end-to-end without writing a single line of code using the Weka open source platform. A step-by-step tutorial approach is used throughout the 18 lessons and 3 end-to-end projects, showing you exactly what to click and exactly what results to expect.

Machine Learning Mastery With Python

Discover the process that you can use to get started and get good at applied machine learning for predictive modeling with the Python ecosystem including Pandas and scikit-learn. This book will lead you from a developer who is interested in machine learning with Python to a developer who has the resources and capability to work through a new dataset end-to-end using Python and develop accurate predictive models.

Machine Learning Mastery With R

Get Started, Build Accurate Models and Work Through Projects Step-by-Step

There’s a reason that R is the most popular platform for applied machine learning for professional data scientists. Discover exactly how to work through a predictive modeling machine learning project step-by-step with R and the widely adopted caret library.

Introduction to Time Series Forecasting With Python

How to Prepare Data and Develop Models to Predict the Future

Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.

Advanced Ebooks

Deep Learning With Python

Develop Deep Learning Models on Theano and TensorFlow Using Keras

Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. Discover exactly how to get started and apply deep learning to your own machine learning projects.

Better Deep Learning

Train Faster, Reduce Overfitting, and Make Better Predictions

Deep learning neural networks have become easy to define and fit, but are still hard to configure. Discover exactly how to improve the performance of deep learning neural network models on your predictive modeling projects. Focus on techniques for faster learning including batch normalization, techniques for less overfitting such as weight decay and dropout, and techniques for better prediction such as stacking ensembles.

Long Short-Term Memory Networks With Python

Develop Sequence Prediction Models With Deep Learning

Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what LSTMs are, and how to develop a suite of LSTM models to get the most out of this modern deep learning algorithm on your sequence prediction problems.

Predict the Future With MLPs, CNNs, and LSTMs in Python

Deep Learning for Natural Language Processing

Develop Deep Learning Models for Natural Language in Python

Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models for your own natural language processing projects.

XGBoost With Python

Gradient Boosted Trees With XGboost and scikit-learn

XGBoost is the dominant technique for predictive modeling on tabular data. The gradient boosting algorithm is the top technique on a wide range of predictive modeling problems, and XGBoost is the fastest implementation. When asked, the best machine learning competitors in the world recommend using XGBoost.